Data-driven optimization for enhanced computational engineering design

用于增强计算工程设计的数据驱动优化

• 批准号: RGPIN-2018-05298
• 负责人: Kokkolaras, Michael
• 金额: $ 4.66万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760208
• 关键词: Data; driven; optimization; enhanced; computational

Data-driven science has been contributing to the advancement of several diverse disciplines ranging from biology to finance. In engineering, this has generated renewed interest in machine learning and artificial intelligence, especially in the context of cyber-physical systems that are developed to enable “industry 4.0,” intelligent transportation systems, or “smart” healthcare systems, just to name a few examples. The common theme is the acquisition and analysis of information in the form of data to better understand, model and predict the behaviour of these collaborative cyber-physical systems so that their design can be improved. This “digital twin” paradigm aims at continuously updating the computational models of their physical counterparts using real-time data to (re-)optimize their design, operation, maintenance, repair or replacement, etc.The availability of such rich contextual data can enhance the computational engineering design process significantly by means of optimization. At the same time, several challenges arise that hinder traditional optimization methods from being applied to this new paradigm, e.g.: To be effective, digital twins are likely to high dimensionality; moreover, they are typically blackboxes: gradient information is typically either not available or almost impossible to approximate reliably. Data sets can be quite large and/or sparse, and can include discontinuities and/or outliers. Moreover, they are appended continuously. The required adequacy of the predictive capability of the models can vary significantly in different areas of the input space spanned by design variables and parameters, especially in light of frequent data updates. The interactions among the connected systems have to be captured and coordinated to ensure that the collaborative network is seamlessly integrated and interoperable. This is especially challenging considering that there exist both physical and computational links among the cyber-physical systems.The objective of the proposed research program is to address the above challenges by developing a framework for data-driven computational engineering design optimization in the context of collaborative cyber-physical systems. To accomplish that, we will develop and integrate a data-driven environment for adaptive, adequacy-based multi-model management and validation with rigorous derivative-free optimization algorithms and coordination techniques for distributed systems. The proposed research program will train 5 PhD students, 3 Masters students, and 5 undergraduate students, preparing them for the next generation of engineering and production systems.

数据驱动的科学一直在为从生物学到金融学等多个不同学科的发展做出贡献。在工程领域，这引起了人们对机器学习和人工智能的新兴趣，特别是在为实现“工业4.0”、智能交通系统或“智能”医疗保健系统而开发的网络物理系统的背景下。共同的主题是获取和分析数据形式的信息，以更好地理解、建模和预测这些协作性网络物理系统的行为，从而改进其设计。这种“数字孪生”模式旨在使用实时数据不断更新其物理对应物的计算模型，以（重新）优化其设计、操作、维护、维修或更换等。这种丰富的上下文数据的可用性可以通过优化显著增强计算工程设计过程。与此同时，出现了一些挑战，这些挑战阻碍了传统的优化方法应用于这种新的范例，例如： 为了有效，数字孪生可能是高维的;此外，它们通常是黑盒：梯度信息通常不可用或几乎不可能可靠地近似。数据集可以是相当大和/或稀疏的，并且可以包括不连续性和/或离群值。此外，它们还在不断地追加。 在设计变量和参数所涵盖的输入空间的不同区域，所需的模型预测能力的充分性可能会有很大差异，特别是在数据更新频繁的情况下。 必须捕获和协调连接系统之间的交互，以确保协作网络无缝集成和互操作。这是特别具有挑战性的，考虑到存在的物理和计算的网络物理systems.The拟议的研究计划的目标之间的联系是通过开发一个框架，数据驱动的计算工程设计优化协同网络物理系统的背景下，以解决上述挑战。为了实现这一目标，我们将开发和集成一个数据驱动的环境，用于自适应，基于充分性的多模型管理和验证，以及严格的无导数优化算法和分布式系统的协调技术。拟议的研究计划将培养5名博士生，3名硕士生和5名本科生，为下一代工程和生产系统做好准备。